5 papers
Minimizing Hyperbolic Embedding Distortion with LLM-Guided Hierarchy Restructuring
Melika Ayoughi, Pascal Mettes, Paul Groth
Hyperbolic geometry is an effective geometry for embedding hierarchical data structures. Hyperbolic learning has therefore become increasingly prominent in machine learning applica…
Continual Hyperbolic Learning of Instances and Classes
Melika Ayoughi, Mina Ghadimi Atigh, Mohammad Mahdi Derakhshani +3
Continual learning has traditionally focused on classifying either instances or classes, but real-world applications, such as robotics and self-driving cars, require models to hand…
Balanced Hyperbolic Embeddings Are Natural Out-of-Distribution Detectors
Tejaswi Kasarla, Max van Spengler, Pascal Mettes
Out-of-distribution recognition forms an important and well-studied problem in deep learning, with the goal to filter out samples that do not belong to the distribution on which a…
How PARTs assemble into wholes: Learning the relative composition of images
Melika Ayoughi, Samira Abnar, Chen Huang +10
The composition of objects and their parts, along with object-object positional relationships, provides a rich source of information for representation learning. Hence, spatial-awa…
Self-Contained Entity Discovery from Captioned Videos
Melika Ayoughi, Pascal Mettes, Paul Groth
This paper introduces the task of visual named entity discovery in videos without the need for task-specific supervision or task-specific external knowledge sources. Assigning spec…